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Hanford begins removing waste from 24th single-shell tank
The Department of Energy’s Office of Environmental Management said crews at the Hanford Site near Richland, Wash., have started retrieving radioactive waste from Tank A-106, a 1-million-gallon underground storage tank built in the 1950s.
Tank A-106 will be the 24th single-shell tank that crews have cleaned out at Hanford, which is home to 177 underground waste storage tanks: 149 single-shell tanks and 28 double-shell tanks. Ranging from 55,000 gallons to more than 1 million gallons in capacity, the tanks hold around 56 million gallons of chemical and radioactive waste resulting from plutonium production at the site.
Josh Peterson, Bret van den Akker, Riley Cumberland, Paul Miller, Kaushik Banerjee
Nuclear Technology | Volume 199 | Number 3 | September 2017 | Pages 310-319
Technical Paper | doi.org/10.1080/00295450.2017.1318595
Articles are hosted by Taylor and Francis Online.
The U.S. Department of Energy, Office of Nuclear Energy is sponsoring development of a database to store information related to spent nuclear fuel (SNF) in support of its Spent Fuel and Waste Disposition efforts. This database, referred to as the Unified Database (UDB), is part of a larger engineering analysis tool, the Used Nuclear Fuel Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS). The UDB provides a comprehensive, controlled source of SNF information, including dry cask attributes, assembly data, economic attributes, transportation infrastructure attributes, potential future facility attributes, and federal government radioactive waste attributes. There are a number of existing and envisioned data reports that can be expected to use data stored within the UDB; however, previously, there was not a streamlined method to couple the database to such data reports. Therefore, to streamline the creation of these reports, two methods were developed to generate documents from information in the database automatically. The first method used Java and LaTeX for automatically generating the report, and the second method used the Python programming language along with Sphinx, a Python documentation generator. There are some advantages and disadvantages to both approaches, but both methods produced equally high-quality, automatically generated reports that were directly coupled to the database. This paper describes data currently available in the UDB; explains the two different methods for automatically generating reports from these data; and shows examples of inline text, figures, and tables automatically generated using both approaches.